%0 Conference Paper %1 he2011citation %A He, Qi %A Kifer, Daniel %A Pei, Jian %A Mitra, Prasenjit %A Giles, C. Lee %B Proceedings of the fourth ACM International Conference on Web Search and Data Mining %C New York, NY, USA %D 2011 %I ACM %K %P 755--764 %R 10.1145/1935826.1935926 %T Citation recommendation without author supervision %U http://doi.acm.org/10.1145/1935826.1935926 %X Automatic recommendation of citations for a manuscript is highly valuable for scholarly activities since it can substantially improve the efficiency and quality of literature search. The prior techniques placed a considerable burden on users, who were required to provide a representative bibliography or to mark passages where citations are needed. In this paper we present a system that considerably reduces this burden: a user simply inputs a query manuscript (<i>without</i> a bibliography) and our system automatically finds locations where citations are needed. We show that na&#239;ve approaches do not work well due to massive noise in the document corpus. We produce a successful approach by carefully examining the relevance between segments in a query manuscript and the representative segments extracted from a document corpus. An extensive empirical evaluation using the CiteSeerX data set shows that our approach is effective. %@ 978-1-4503-0493-1

@inproceedings{he2011citation, abstract = {Automatic recommendation of citations for a manuscript is highly valuable for scholarly activities since it can substantially improve the efficiency and quality of literature search. The prior techniques placed a considerable burden on users, who were required to provide a representative bibliography or to mark passages where citations are needed. In this paper we present a system that considerably reduces this burden: a user simply inputs a query manuscript (<i>without</i> a bibliography) and our system automatically finds locations where citations are needed. We show that na&#239;ve approaches do not work well due to massive noise in the document corpus. We produce a successful approach by carefully examining the relevance between segments in a query manuscript and the representative segments extracted from a document corpus. An extensive empirical evaluation using the CiteSeerX data set shows that our approach is effective.}, acmid = {1935926}, added-at = {2014-01-13T10:03:00.000+0100}, address = {New York, NY, USA}, author = {He, Qi and Kifer, Daniel and Pei, Jian and Mitra, Prasenjit and Giles, C. Lee}, biburl = {https://www.bibsonomy.org/bibtex/26b9c528f61a7d4d7f99508b8451a600d/jaeschke}, booktitle = {Proceedings of the fourth ACM International Conference on Web Search and Data Mining}, doi = {10.1145/1935826.1935926}, interhash = {7e98aaf26a7ed6cc624249a3ab570d7a}, intrahash = {6b9c528f61a7d4d7f99508b8451a600d}, isbn = {978-1-4503-0493-1}, keywords = {}, location = {Hong Kong, China}, numpages = {10}, pages = {755--764}, publisher = {ACM}, timestamp = {2014-01-13T10:03:00.000+0100}, title = {Citation recommendation without author supervision}, url = {http://doi.acm.org/10.1145/1935826.1935926}, year = 2011 }